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A Hybrid Data-driven Deep Learning Technique for Fluid-Structure Interaction

A Hybrid Data-driven Deep Learning Technique for Fluid-Structure Interaction

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2092809719

A Hybrid Data-driven Deep Learning Technique for Fluid-Structure Interaction

About this item

Full title

A Hybrid Data-driven Deep Learning Technique for Fluid-Structure Interaction

Author / Creator

Publisher

Ithaca: Cornell University Library, arXiv.org

Journal title

arXiv.org, 2019-02

Language

English

Formats

Publication information

Publisher

Ithaca: Cornell University Library, arXiv.org

More information

Scope and Contents

Contents

This paper is concerned with the development of a hybrid data-driven technique for unsteady fluid-structure interaction systems. The proposed data-driven technique combines the deep learning framework with a projection-based low-order modeling. While the deep learning provides low-dimensional approximations from datasets arising from black-box solv...

Alternative Titles

Full title

A Hybrid Data-driven Deep Learning Technique for Fluid-Structure Interaction

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2092809719

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2092809719

Other Identifiers

E-ISSN

2331-8422

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